mesh-flow (version 2) for general ai agent (openclaw, hermes agent)
by Roy Yuen
Replace fragile prompt-chains with a strict, artifact-driven DAG orchestration system for reliable agent workflows.
- Define strict multi-agent dependencies via YAML-based DAGs
- Enforce human-in-the-loop gates and artifact contracts between steps
- Generate Mermaid visualizations and execution traces for complex flows
$8
One-time purchase · Own forever
Included in download
- Define strict multi-agent dependencies via YAML-based DAGs
- Enforce human-in-the-loop gates and artifact contracts between steps
- terminal, file_read, file_write automation included
- Includes example output and usage patterns
See it in action
Compiled execution plan from project.yaml: - Success: 5 nodes validated - Graph: contract -> plan -> (implement, review) -> verify - Gates: [human_approval] detected on 'review' node. Execution: Node [draft-plan] started. Consuming [contract]. Success. Produced [plan].
mesh-flow (version 2) for general ai agent (openclaw, hermes agent)
by Roy Yuen
Replace fragile prompt-chains with a strict, artifact-driven DAG orchestration system for reliable agent workflows.
$8
One-time purchase · Own forever
⚡ Also available via Agensi MCP — your AI agent can load this skill on demand via MCP. Learn more →
Included in download
- Define strict multi-agent dependencies via YAML-based DAGs
- Enforce human-in-the-loop gates and artifact contracts between steps
- terminal, file_read, file_write automation included
- Includes example output and usage patterns
- Instant install
See it in action
Compiled execution plan from project.yaml: - Success: 5 nodes validated - Graph: contract -> plan -> (implement, review) -> verify - Gates: [human_approval] detected on 'review' node. Execution: Node [draft-plan] started. Consuming [contract]. Success. Produced [plan].
About This Skill
Artifact-Driven DAG Orchestration
Complexity in AI agents often stems from implicit prompt-chaining where flow logic is buried inside instructions. mesh-flow solves this by introducing a strict, compile-then-run DAG (Directed Acyclic Graph) architecture. It decouples the flow topology from individual node reasoning, ensuring your agents follow a predictable, reproducible, and verifiable path.
What it does
This skill provides a robust framework for building complex agentic workflows using artifact-driven modeling. Instead of telling an agent to "then do X," you define what artifacts a node consumes and what it produces. The system uses a dedicated CLI to validate dependencies, detect cycles, and compile your YAML definitions into a normalized execution plan.
- Explicit Topology: Uses
project.yamlas the single source of truth for your flow logic. - Hard Gates: Enforces runtime logic (like human approval or upstream success) that prompts cannot hallucinate their way through.
- State Machine Execution: Manages node states (failed, blocked, rejected) with explicit recovery paths.
- Standardized Tracing: Generates detailed execution traces for every node, including prompt templates and tool calls.
Why use this skill
Unlike standard prompting, mesh-flow provides a structural "spine" for your agents. It prevents flow drift, enables shadow-mode testing for complex migrations, and provides a CLI for local validation and Mermaid diagram generation. It is ideal for developers building multi-step pipelines where reliability and auditability are non-negotiable.
📖 Learn more: Best DevOps & Deployment Skills for Claude Code →
Use Cases
- Define strict multi-agent dependencies via YAML-based DAGs
- Enforce human-in-the-loop gates and artifact contracts between steps
- Generate Mermaid visualizations and execution traces for complex flows
- Validate workflow topology to prevent cycles and missing dependencies
How to Install
unzip mesh-flow-version-2-for-general-ai-agent-openclaw-hermes-agent.zip -d ~/.claude/skills/Reviews
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